## Abstract

When simple parametric models such as linear regression fail to adequately approximate a relationship across an entire set of data, an alternative may be to consider a partition of the data, and then use a separate simple model within each subset of the partition. Such an alternative is provided by a treed model which uses a binary tree to identify such a partition. However, treed models go further than conventional trees (e.g. CART, C4.5) by fitting models rather than a simple mean or proportion within each subset. In this paper, we propose a Bayesian approach for finding and fitting parametric treed models, in particular focusing on Bayesian treed regression. The potential of this approach is illustrated by a cross-validation comparison of predictive performance with neural nets, MARS, and conventional trees on simulated and real data sets.

Original language | English (US) |
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Pages (from-to) | 299-320 |

Number of pages | 22 |

Journal | Machine Learning |

Volume | 48 |

Issue number | 1-3 |

DOIs | |

State | Published - Jul 2002 |

Externally published | Yes |

## Keywords

- Binary trees
- Markov chain Monte Carlo
- Model selection
- Stochastic search

## ASJC Scopus subject areas

- Software
- Artificial Intelligence